Hepatic venous pressure gradient measurement: is it mandatory in the management of portal hypertension?
Bibliographic record
Abstract
Abstract Portal hypertension can be evaluated by hepatic vein catheterization and measurement of wedged and free hepatic vein pressures. The hepatic venous pressure gradient (HVPG) is the difference between both pressures and its normal value is lower than 5 mmHg. The technique is safe and reliable provided several requirements are fulfilled to get accurate results. HVPG measurement is useful to determine the site of increased resistance either presinusoidal, sinusoidal or postsinusoidal. If HVPG is normal in the presence of clinical signs of portal hypertension, evaluation of the portal venous system and direct measurement of portal vein pressure is required. HVPG measurement may also be used as a prognostic marker to evaluate the risks of developing complications such as ascites or variceal bleeding; in addition, it has been suggested that it could provide prognostic information for variceal rebleeding or survival. Primary and secondary prophylaxis of variceal bleeding can be achieved with a pharmacological treatment using beta blockers and/or nitrates. Repeated HVPG measurements are probably useful to monitor the treatment; it has been suggested that decreasing HVPG by 20% or below 12 mmHg is a reasonable target to define a good hemodynamic response and hopefully a low risk of bleeding; endoscopic therapy can be used in non‐responders. Repeated hemodynamic evaluation, however, is invasive and must be performed in specialized liver units; therefore, future clinical trials must demonstrate unequivocally the clinical usefulness of this approach prior to recommending repeated HVPG measurement on a routine basis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".